Continuous Casting Tundish Dead Volume Study by Physical Modeling and Computational Investigation
Bibliographic record
Abstract
Flow efficiency in a two‐strand continuous casting tundish is studied by analyzing the residential time distribution (RTD) curves in a small‐scale tundish water model using a conductive NaCl solution tracer. The velocity fields in the tundish water model are measured by particle image velocimetry, which is used to validate the results of the mathematical model in the article. It is found that the tracer concentration has a significant impact on the predicted dead volume fraction in the RTD analysis. Validated mathematical modeling of the computational fluid dynamics (CFD) technology is performed to explore the root cause of the defective results in the RTD analysis. It is found that the flow inside the tundish is sensitive to density variations caused by the injected tracer. A denser tracer will stay lower in the tundish by gravity and flow out of the tundish more quickly. A proper tracer concentration in the water model experiments is discussed to visualize the dead volume and improve tundish furniture design efficiently for future work, a new method using CFD modeling is proposed in this article, which can directly demonstrate the dead volume's location.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".